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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Hierarchic neighbors embedding

arXiv

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Prospects for the use of wireless reading of prosthetic control signals and methods for generating commands

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IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： D Malakhov V Zhmud V Avrmachuk D Gryaznov J Nosek L Dimitrov Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Mechatronics and Computer Engineering (MTI) Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Vice rector Technical University of Sofia Sofia Bulgaria

The paper provides a rationale for the use of low-power wireless communication to create non-invasive bionic limb prostheses. The need for such a connection is dictated, on the one hand, by the expediency of using sensors located in various parts of the body, often very remote from each other, on the other hand, by the desire to free the disabled person's body from the wires that would envelop it like a spider web. For each of these positions, arguments are given.

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Prospects for the development of functional expansion of individual low-cost prosthetic limbs for people with disabilities with intelligent sensors

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IOP Conference Series: Materials Science and engineering 2021年第1期1019卷

作者： D Gryaznov V Zhmud V Avrmachuk D Malakhov J Nosek L Dimitrov Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Complex Information Security of Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Mechatronics and Computer Engineering (MTI) Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Liberec Czech Republic Vice rector of Technical University of Sofia Sofia Bulgaria

Despite the development of medicine, as well as robotics aimed at helping people with disabilities, most disabled people with missing limbs cannot hope for a full life with the help of bionic prostheses, since these products are made strictly individually, and their control requires expensive surgeries to implant sensors. In addition, such prostheses themselves are very expensive. An alternative solution is to reduce the cost of such prostheses as much as possible and abandon the implantable sensors of signals of the human nervous system. The paper proposes a method for solving such a problem using a variety of sensors that remove signals from the skin surface without invasive surgery.

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Multivariable Fractional Order PI Autotuning Method for Heterogeneous Dynamic Systems

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IFAC-PapersOnLine 2018年第4期51卷 865-870页

作者： Cajo, Ricardo Muresan, Cristina I. Ionescu, Clara M. De Keyser, Robin Plaza, Douglas Ghent University Faculty of Engineering and Architecture Research group on Dynamical Systems Control Technologiepark 914 ZwijnaardeB9052 Belgium Escuela Superior Politecnica del Litoral ESPOL Grupo de Investigación en Automatización y Control Industrial Campus Gustavo Galindo Km 30.5 Vía Perimetral Guayaquil09-01-5863 Ecuador Technical University of Cluj-Napoca Romania Department of Automation Memorandumului Street No.28 Cluj-Napoca Romania

In this paper the application of robust Fractional Order Proportional-Integral (FO-PI) autotuning control strategy is presented and applied to heterogeneous dynamic systems using decentralized control. The automatic tuning of controller gains is based on a single sine test, with user-defined robustness margins guaranteed. Its performance is compared against two other fractional order controllers based on PI gain-crossover autotuning method and Internal Model control (IMC). The closed loop control simulations applied on the heterogeneous dynamic systems indicate that the proposed method performs properly. © 2018

关键词： controllers Closed loop control systems Decentralized control Dynamical systems MIMO systems Model predictive control Robustness (control systems) Two term control systems Autotuner Fractional order pI Heterogeneous dynamics Imc controllers Multi input multi output

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The learning of fuzzy cognitive maps with noisy data: A rapid and robust learning method with maximum entropy

arXiv

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arXiv 2019年

作者： Feng, Guoliang Lu, Wei Pedrycz, Witold Yang, Jianhua Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116023 China School of Automation Engineering Northeast Electric Power University Jilin132012 China School of Control Science and Engineering Dalian University of Technology Dalian116023 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia

Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dynamic systems. However, these methods are faced with several obvious limitations. Most of these models are extremely time consuming when learning the large-scale FCMs with hundreds of nodes. Furthermore, the FCMs learned by those algorithms lack robustness when the experimental data contain noise. In addition, reasonable distribution of the weights is rarely considered in these algorithms, which could result in the reduction of the performance of the resulting FCM. In this article, a straightforward, rapid, and robust learning method is proposed to learn FCMs from noisy data, especially, to learn large-scale FCMs. The crux of the proposed algorithm is to equivalently transform the learning problem of FCMs to a classic-constrained convex optimization problem in which the least-squares term ensures the robustness of the well-learned FCM and the maximum entropy term regularizes the distribution of the weights of the well-learned FCM. A series of experiments covering two frequently used activation functions (the sigmoid and hyperbolic tangent functions) are performed on both synthetic datasets with noise and real-world datasets. The experimental results show that the proposed method is rapid and robust against data containing noise and that the well-learned weights have better distribution. In addition, the FCMs learned by the proposed method also exhibit superior performance in comparison with the existing methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Fuzzy rules

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Short term electric load forecast with artificial neural networks

arXiv

引用

arXiv 2018年

作者： Cristian, Vasar Iosif, Szeidert Ioan, Filip Gabriela, Prostean Department of Control System Engineering Politehnica' University from Timisoara Faculty of Automation and Computer Science Timisoara300223 Romania

This paper presents issues regarding short term electric load forecasting using feedforward and Elman recurrent neural networks. The study cases were developed using measured data representing electrical energy consume from Banat area. There were considered 35 different types of structure for both feedforward and recurrent network cases. For each type of neural network structure were performed many trainings and best solution was selected. The issue of forecasting the load on short term is essential in the effective energetic consume management in an open market environment. Copyright © 2007 IFAC Copyright © 2018, The Authors. All rights reserved.

关键词： Electric load forecasting

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Sparse Least Square Support Vector Machines based on Random Entropy 36

Sparse Least Square Support Vector Machines based on Random ...

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第36届中国控制会议

作者： Wenlu Ma Han Liu Faculty of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Lab. of Complex System Control and Intelligent Information Processing Faculty of Automation and Information EngineeringXi’an University of Technology

ISBN: (纸本)9781538629185

Least squares support vector machines(LSSVM) has a good performance in small data samples, but can't solve the large-scale sample problems. In this paper, large data set sparse least squares support vector machines model based on stochastic entropy is proposed, and it can be applied to large-scale data samples. Firstly, the large-scale data set is divided into several subsets. Then the entropy method is used to the sparse samples in each subset. Finally, we use sparse samples sets as training samples, and use least squares support vector machine algorithm to train. The results show that the sparse least squares support vector machine model based on entropy can effectively solve the problem of large-scale data.

关键词： Sparsity LSSVM Large-scale data Random Entropy

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New Grid for Particle Filtering of Multivariable Nonlinear Objects

New Grid for Particle Filtering of Multivariable Nonlinear O...

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Federated Conference on Computer Science and Information Systems

作者： Piotr Kozierski Jacek Michalski Talar Sadalla Wojciech Giernacki Joanna Zietkiewicz Szymon Drgas Faculty of Computing Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Faculty of Electrical Engineering Institute of Control Robotics and Information Engineering Division of Control and Robotics Poznan University of Technology

ISBN: (纸本)9781538623718

In the paper a new grid (potentially linear, nonlinear and even semi-Markovian jump system) was presented. All transition and measurement functions were proposed. Moreover, the transition functions of two types were considered - dependent on one and many different state variables. Also 10 types of measurements were proposed for both nodal and branch cases. Based on the obtained results one can see, which measurement functions are "easy", and which are "hard" for state estimation task.

关键词： Measuring Function Particle filters grids Grid State variable Multivariable nonlinearity

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Sizing of a sustainable condensation system for the production of sweet water through the use of photovoltaic panels in La Mesa de los Santos, Santander, Colombia

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IOP Conference Series: Materials Science and engineering 2021年第1期1154卷

作者： B E Tarazona-Romero A-J Rodriguez Nieves C L Sandoval-Rodriguez J G Ascanio-Villabona M-A Duran-Sarmiento A-D Rincon-Quintero Automation and Control Energy Systems Research Group (GISEAC) Faculty of Natural Sciences and Engineering Electromechanical engineering Unidades Tecnologicas de Santander (UTS) Student Street No 9-82 680005 Bucaramanga Colombia Energy in Building research group (ENEDI) Doctoral Program in Energy Efficiency and Sustainability in Engineering and Architecture Department of Machines and Thermal Engines University of the Basque Country (UPV / EHU) Engineer Torres Quevedo Square 1 48013 Bilbao BI Spain Doctoral Program in Electronic and Communications Department of Communications Engineering University of the Basque Country UPV/EHU 48013 Bilbao Spain

The objective of the project is to carry out the dimensioning of a photovoltaic system that allows its integration with a condensation system for the production of fresh water in isolated areas (Rural), in such a way that a sustainable condensation application capable of producing water is generated. sweet to supply small groups of inhabitants in areas where the basic supply of liquid is deficient. The study area is the Department of Santander, Colombia, specifically in the Municipality of los Santos and the area known as Mesa de los Santos, in which difficult geographical conditions are presented such as: altitude of 1650 meters above sea level and deficiency in aquifers. The methodological development proposes the analysis of the meteorological conditions of the area, the identification, selection and dimensioning of the condensing system and the photovoltaic system through the amp-hour method and the software system advisor model (SAM). Additionally, a CAD model is developed to obtain the complete schematization of the model and carry out a simulation process to analyze the behavior of the integration of the aforementioned systems. Finally, a techno-economic analysis of the proposed system is carried out.

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